Integration of refugees into the European education and labour market : requirements for a target group oriented approach
Bibliographic record
Abstract
Contents: Louis Henri Seukwa: General introduction - Margherita Buchetti/Rita Cardini/Daria Franceschini/Chiara Trevisan/Maria Omodeo Nicola Solimano/Claudia Zaccai: The paradox of being a recognized refugee in Italy: Living in an open prison, Florence, Tuscany - Pamela Clayton/Paul McGill/Sarah-Jane Pretty: A life in limbo: Barriers to VET and labour market integration for asylum-seekers waiting for the granting of Leave to Remain - Maren Gag: Vocational integration of refugees and Asylum-seekers in Hamburg - roundabout routes from model to structure - Maren Gag/Joachim Schroeder: Refugee Monitoring: Research status, conceptual basis and implementation proposals, taking the example of the City of Hamburg - Maren Gag/Joachim Schroeder: Refugee Monitoring in Hamburg - first steps taken! - Eva Norstrom/Anna Norberg: The reception and introduction of asylum seekers and new arrivals in Gothenburg. Successes and failures in the development of a new system - Maren Gag/Paul McGill/Eva Norstrom/Maria Omodeo/Sarah Jane Pretty/Maike Schroder/Louis Henri Seukwa/Claudia Zaccai: Lessons learned: Recommendations on the European level and conclusive remarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".